Weaning Outcome Prediction from Heterogeneous Time Series using Normalized Compression Distance and Multidimensional Scaling

In the Intensive Care Unit of a hospital (ICU), weaning can be de ned as
the process of gradual reduction in the level of mechanical ventilation support.
A failed weaning increases the risk of death in prolonged mechanical ventilation
patients. Di erent methods for weaning outcome prediction have been proposed
using variables and time series extracted from the monitoring systems,
however, monitored data are often non-regularly sampled, hence limiting its
use in conventional automatic prediction systems. In this work, we propose the
joint use of two statistical techniques, Normalized Compression Distance (NCD)
and Multidimensional Scaling (MDS), to deal with data heterogeneity in monitoring
systems for weaning outcome prediction. A total of 104 weanings were
selected from 93 patients under mechanical ventilation from the ICU of Hospital
Universitario Fundaci on Alcorc on; for each weaning, time series (TS), clinical
laboratory and general descriptors variables were collected during 48 hours previous
to the moment of withdrawal mechanical support (extubation). The TS
diastolic blood pressure variable provided the best weaning prediction, with an improvement of 37% in the error rate regarding the physician decision. This
result shows that the joint use of the NCD and MDS e ciently discriminates
heterogeneous time series.